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Record W3164562273 · doi:10.1177/08919887211016068

Plasma β-Amyloid in Mild Behavioural Impairment – Neuropsychiatric Symptoms on the Alzheimer’s Continuum

2021· article· en· W3164562273 on OpenAlexafffund
Ruxin Miao, Hung‐Yu Chen, Sascha Gill, James Naude, Eric E. Smith, Zahinoor Ismail

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsDementiaCognitionPsychologyAlzheimer's diseaseCognitive impairmentNeuroimagingDiseaseInternal medicineCognitive declinePsychiatryMedicineClinical psychology

Abstract

fetched live from OpenAlex

Introduction: Simple markers are required to recognize older adults at higher risk for neurodegenerative disease. Mild behavioural impairment (MBI) and plasma β-amyloid (Aβ) have been independently implicated in the development of incident cognitive decline and dementia. Here we studied the associations between MBI and plasma Aβ 42 /Aβ 40 . Methods: Participants with normal cognition (n = 86) or mild cognitive impairment (n = 53) were selected from the Alzheimer’s Disease Neuroimaging Initiative. MBI scores were derived from Neuropsychiatric Inventory items. Plasma Aβ 42 /Aβ 40 ratios were assayed using mass spectrometry. Linear regressions were fitted to assess the association between MBI total score as well as MBI domain scores with plasma Aβ 42 /Aβ 40 . Results: Lower plasma Aβ 42 /Aβ 40 was associated with higher MBI total score ( p = 0.04) and greater affective dysregulation ( p = 0.04), but not with impaired drive/motivation ( p = 0.095) or impulse dyscontrol ( p = 0.29) MBI domains. Conclusion: In persons with normal cognition or mild cognitive impairment, MBI was associated with low plasma Aβ 42 /Aβ 40 . Incorporating MBI into case detection may help capture preclinical and prodromal Alzheimer’s disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.285
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations89
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Geriatric Psychiatry and NeurologySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207